The subject application relates to targeted advertising systems and methods. While the systems and methods described herein relate to targeted advertising and the like, it will be appreciated that the described techniques may find application in other advertising systems, other advertising pricing applications, and/or other advertisement placement and pricing methods.
Advertisers are constantly searching for more efficient means to allow their products/services to be advertised to consumers who have a need for their products/services or who are currently spending money on similar products/services with a competitor. In essence, advertisers are constantly faced with finding the consumer who is “ready” to purchase their products/services, “able” to complete the purchase of their products/services, and “willing” to purchase the products/services immediately (“RAW”). With the advent of newer technologies such as Digital Video Recorders (DVRs) and On Demand TV, advertisers fear the loss of traditional means of advertising to consumers.
Current options for electronic web based advertising are very costly and yield limited results. To reach an acceptable number of consumers to advertise their products/services advertisers must run campaigns, which reach masses of people but only yield single digit return in consumer interest and purchases. For example out of 100 consumers who see an advertiser's campaign/ad, only 3-5 may be RAW. Despite their best efforts, current consumer advertising methods remain very costly and yield a minimal return for the amount of investment.
One problem with present online advertising is called a shotgun approach. In this approach, an advertiser/marketer buys a word that is typed into an internet search engine. When purchasing this word or phrase the marketer/advertiser is thinking, “based on this word I think that xx % might be interested in my product”. The problem is those words are very expensive. Internet advertisers are getting market rates such as between $5.00 and $10.00 per word per click to have a good placement on a web site per a single word or two word phrase. Moreover, the marketer/advertiser does not know whether the consumer who views their advertisement is RAW.
Marketers have been able to develop ways to maintain customers once they have initiated purchases via tracking of the consumers purchasing habits and trends. Special loyalty programs have been developed such as reward coupons and other incentives based on the amount, frequency, and trends of the consumers purchases. Other advertisers/on-line retailers such as Amazon.com profile the customers who actually visit their site. With Amazon, the consumer is profiled by the products they view and what they purchase while on the site. Amazon then tracks the consumer's habits of shopping and what they purchased so that when the consumer signs in at another time advertisements will immediately pop up with “suggested items” for the consumer to consider purchasing based on their previous actions and purchases with Amazon only. While these approaches are effective in maintaining already existing customers and motivating the consumer to purchase additional items, they fall short in their ability to secure additional customers from competitors who offer similar products/services. The current advertising methodologies are still unable to track actual consumer spending and trends outside of an already existing customer, i.e. based on a broad spectrum of actual financial transactions within the consumer's financial institution(s).
To make their advertising dollars more effective, advertisers attempt to target their advertising to individuals who are more likely to have an interest in the advertised product, thereby producing a higher click-through rate and increased revenues. Of course, in order to target individuals with any degree of accuracy, something must be known about the individual. For this reason, technologies have been developed for what is known in the art as behavioral targeting based on tracking a user's habits through monitoring of the websites that the user visits, and offering targeted advertising based on the content of the visited websites. It is assumed, for example, that if a user is visiting automobile oriented websites, then an automobile oriented advertisement is more likely to generate a user response than one for breakfast cereal. A problem with this type of website tracking is that if an automobile advertisement for a very expensive car is delivered to a user and he cannot afford to purchase the automobile, then the advertisement is not very effective.
There is a need for a personalized advertising architecture/solution, which facilitates permitting a user to view targeted offers, select store target offers for later review, and recall a library of targeted offers for later review and/or acceptance.